# Inverse Statistical Physics of Protein Sequences: A Key Issues Review

## TL;DR

This review discusses how inverse statistical physics applied to protein sequences helps infer evolutionary constraints, aiding understanding of protein structure and function from large sequence datasets.

## Contribution

It provides an overview of how statistical-mechanics inspired models are used to analyze protein sequences and address key biological questions.

## Key findings

- Sequence data enables inference of evolutionary constraints.
- Models reveal links between sequence variability and protein structure.
- Open questions in the field are identified for future research.

## Abstract

In the course of evolution, proteins undergo important changes in their amino acid sequences, while their three-dimensional folded structure and their biological function remain remarkably conserved. Thanks to modern sequencing techniques, sequence data accumulate at unprecedented pace. This provides large sets of so-called homologous, i.e.~evolutionarily related protein sequences, to which methods of inverse statistical physics can be applied. Using sequence data as the basis for the inference of Boltzmann distributions from samples of microscopic configurations or observables, it is possible to extract information about evolutionary constraints and thus protein function and structure. Here we give an overview over some biologically important questions, and how statistical-mechanics inspired modeling approaches can help to answer them. Finally, we discuss some open questions, which we expect to be addressed over the next years.